added new github examples to dataset

README.md

@@ -227,15 +227,39 @@ configs:
   - "pbt/cc_pendulum_sac.csv"
 ---
 
-# The ARLBench Performance Dataset
+# **The ARLBench Performance Dataset**
 
-[ARLBench](https://github.com/automl/arlbench) is a benchmark for hyperparameter optimization in Reinforcement Learning. 
-Since we performed several thousand runs on the benchmark to find meaningful HPO test settings in RL, we collect them in this dataset for future use.
-These runs could be used to meta-learn information about the hyperparameter landscape or warmstart HPO tools.
+[**ARLBench**](https://github.com/automl/arlbench) is a benchmark designed for **hyperparameter optimization (HPO) in Reinforcement Learning (RL)**. Given that we conducted several thousand runs to identify meaningful HPO test settings for RL, we have compiled these results into a dataset for future research and applications.
 
-In detail, it contains each 10 runs for the landscape data of PPO, DQN and SAC respectively on the Atari-5 environments, four XLand gridworlds, four Brax walkers, five classic control and two Box2D environments.
-Additionally, it contains each 3 runs for the 5 optimzation algorithms PBT, SMAC, SMAC with Multi-Fidelity and Random Search for each algorithm and environment pair. 
-The dataset follows the mapping: 
-$$\text{Training Budget and Seed, Hyperparameter Configuration} \mapsto \text{Training Performance}$$
-For the optimization runs, it additionally includes the key *optimization seed* to distinguish configurations between the 5 optimization runs for each algorithm/environment pair.
-For more information, refer to the [ARLBench](https://arxiv.org/abs/2409.18827) paper.
+This dataset can be leveraged to:
+- **Meta-learn insights** about the hyperparameter landscape in RL.
+- **Warm-start HPO tools** by utilizing previously explored configurations.
+
+### **Dataset Details**
+The dataset includes:
+- **Landscape data:** 10 runs each for PPO, DQN, and SAC across:
+  - Atari-5 environments
+  - Four XLand gridworlds
+  - Four Brax walkers
+  - Five classic control environments
+  - Two Box2D environments
+- **Optimization data:** 3 runs per optimization algorithm for each algorithm-environment combination, covering:
+  - Population-Based Training (PBT)
+  - SMAC
+  - SMAC with Multi-Fidelity
+  - Random Search
+
+### **Dataset Mapping**
+The dataset follows this mapping:
+$$\text{training steps, seed, hyperparameter configuration} \mapsto \text{training performance}$$
+
+For optimization runs, it additionally includes:
+- **Optimization seed**: Differentiates between the five optimization runs per algorithm-environment pair.
+- **Optimization step**: Tracks configurations evaluated at different steps.
+
+### **Example Usage**
+You can find example notebooks demonstrating how to use:
+- **[Landscape data](https://github.com/automl/arlbench/blob/main/examples/landscape_analysis.ipynb)**
+- **[Optimization data](https://github.com/automl/arlbench/blob/main/examples/optimization_data_analysis.ipynb)**
+
+For more details, refer to the **[ARLBench paper](https://arxiv.org/abs/2409.18827)**.